Ian H. Witten – författare

Web Dragons offers a perspective on the world of Web search and the effects of search engines and information availability on the present and future world.In the blink of an eye since the turn of the millennium, the lives of people who work with information have been utterly transformed. Everything we need to know is on the web. It''s where we learn and play, shop and do business, keep up with old friends and meet new ones. Search engines make it possible for us to find the stuff we need to know. Search engines — web dragons — are the portals through which we access society''s treasure trove of information. How do they stack up against librarians, the gatekeepers over centuries past? What role will libraries play in a world whose information is ruled by the web? How is the web organized? Who controls its contents, and how do they do it? How do search engines work? How can web visibility be exploited by those who want to sell us their wares? What''s coming tomorrow, and can we influence it?As we witness the dawn of a new era, this book shows readers what it will look like and how it will change their world. Whoever you are: if you care about information, this book will open your eyes and make you blink.- Presents a critical view of the idea of funneling information access through a small handful of gateways and the notion of a centralized index--and the problems that may cause- Provides promising approaches for addressing the problems, such as the personalization of web services- Presented by authorities in the field of digital libraries, web history, machine learning, and web and data mining- Find more information at the author''s site: webdragons.net

Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references.The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more.This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses.- Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods- Performance improvement techniques that work by transforming the input or output

Given modern society''s need to control its ever-increasing body of information, digital libraries will be among the most important and influential institutions of this century. With their versatility, accessibility, and economy, these focused collections of everything digital are fast becoming the "banks" in which the world''s wealth of information is stored.How to Build a Digital Library is the only book that offers all the knowledge and tools needed to construct and maintain a digital library-no matter how large or small. Two internationally recognized experts provide a fully developed, step-by-step method, as well as the software that makes it all possible. How to Build a Digital Library is the perfectly self-contained resource for individuals, agencies, and institutions wishing to put this powerful tool to work in their burgeoning information treasuries.- Sketches the history of libraries-both traditional and digital-and their impact on present practices and future directions- Offers in-depth coverage of today''s practical standards used to represent and store information digitally- Uses Greenstone, freely accessible open-source software-available with interfaces in the world''s major languages (including Spanish, Chinese, and Arabic)- Written for both technical and non-technical audiences

This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining. This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources.- Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints.- Presents multiple methods of analysis and algorithmic problem-solving techniques, enhancing the reader''s technical expertise and ability to implement practical solutions.- Coverage of both theory and practice brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases.

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.- Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects- Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods- Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

How to Build a Digital Library reviews knowledge and tools to construct and maintain a digital library, regardless of the size or purpose. A resource for individuals, agencies, and institutions wishing to put this powerful tool to work in their burgeoning information treasuries.The Second Edition reflects developments in the field as well as in the Greenstone Digital Library open source software. In Part I, the authors have added an entire new chapter on user groups, user support, collaborative browsing, user contributions, and so on. There is also new material on content-based queries, map-based queries, cross-media queries. There is an increased emphasis placed on multimedia by adding a "digitizing" section to each major media type. A new chapter has also been added on "internationalization," which will address Unicode standards, multi-language interfaces and collections, and issues with non-European languages (Chinese, Hindi, etc.).Part II, the software tools section, has been completely rewritten to reflect the new developments in Greenstone Digital Library Software, an internationally popular open source software tool with a comprehensive graphical facility for creating and maintaining digital libraries.- Outlines the history of libraries on both traditional and digital- Written for both technical and non-technical audiences and covers the entire spectrum of media, including text, images, audio, video, and related XML standards- Web-enhanced with software documentation, color illustrations, full-text index, source code, and more

Web Dragons offers a perspective on the world of Web search and the effects of search engines and information availability on the present and future world.In the blink of an eye since the turn of the millennium, the lives of people who work with information have been utterly transformed. Everything we need to know is on the web. It's where we learn and play, shop and do business, keep up with old friends and meet new ones. Search engines make it possible for us to find the stuff we need to know. Search engines — web dragons — are the portals through which we access society's treasure trove of information. How do they stack up against librarians, the gatekeepers over centuries past? What role will libraries play in a world whose information is ruled by the web? How is the web organized? Who controls its contents, and how do they do it? How do search engines work? How can web visibility be exploited by those who want to sell us their wares? What's coming tomorrow, and can we influence it?As we witness the dawn of a new era, this book shows readers what it will look like and how it will change their world. Whoever you are: if you care about information, this book will open your eyes and make you blink.

This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining. This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources. Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints. Presents multiple methods of analysis and algorithmic problem-solving techniques, enhancing the reader's technical expertise and ability to implement practical solutions. Coverage of both theory and practice brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases.

How to Build a Digital Library reviews knowledge and tools to construct and maintain a digital library, regardless of the size or purpose. A resource for individuals, agencies, and institutions wishing to put this powerful tool to work in their burgeoning information treasuries.

The Second Edition reflects developments in the field as well as in the Greenstone Digital Library open source software. In Part I, the authors have added an entire new chapter on user groups, user support, collaborative browsing, user contributions, and so on. There is also new material on content-based queries, map-based queries, cross-media queries. There is an increased emphasis placed on multimedia by adding a "digitizing" section to each major media type. A new chapter has also been added on "internationalization," which will address Unicode standards, multi-language interfaces and collections, and issues with non-European languages (Chinese, Hindi, etc.).

Part II, the software tools section, has been completely rewritten to reflect the new developments in Greenstone Digital Library Software, an internationally popular open source software tool with a comprehensive graphical facility for creating and maintaining digital libraries.

Outlines the history of libraries on both traditional and digital Written for both technical and non-technical audiences and covers the entire spectrum of media, including text, images, audio, video, and related XML standards Web-enhanced with software documentation, color illustrations, full-text index, source code, and more

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today''s techniques coupled with the methods at the leading edge of contemporary research.Please visit the book companion website at https://www.cs.waikato.ac.nz/~ml/weka/book.html.It contains- Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book- Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book- Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.- Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects- Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods- Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface- Includes open-access online courses that introduce practical applications of the material in the book

**2026 Textbook and Academic Authors Association (TAA) Textbook Excellence "Texty" Award Winner**Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on topics such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today’s techniques coupled with the methods at the leading edge of contemporary research

Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projectsPresents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methodsFeatures in-depth information on deep learning and probabilistic modelsCovers performance improvement techniques, including input preprocessing and combining output from different methodsProvides an appendix introducing the WEKA machine learning workbench and links to algorithm implementations in the softwareIncludes all-new exercises for each chapter

Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on topics such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today''s techniques coupled with the methods at the leading edge of contemporary research- Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects- Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods- Features in-depth information on deep learning and probabilistic models- Covers performance improvement techniques, including input preprocessing and combining output from different methods- Provides an appendix introducing the WEKA machine learning workbench and links to algorithm implementations in the software- Includes all-new exercises for each chapter

For people with various forms of physical disability, extreme slowness of communication is commonplace. In the first part of this 1992 book a functional architecture for communication aids is discussed and the idea of automatically supplying the intrinsic redundancy contained in natural communication is explained. The distinctions between adaptive and non-adaptive models of communication are shown and details are given of working predictive text generation systems. One such system is the Reactive Keyboard, and in the second part of the book this is described. It greatly speeds communication by predicting the user's next response, although it does not always predict correctly. The guesses are made on the basis of previous answers and thus can conform to whatever kind of text is entered. This will be of great value to all involved in helping disabled users interact with computers.

For people with various forms of physical disability, extreme slowness of communication is commonplace. In the first part of this 1992 book a functional architecture for communication aids is discussed and the idea of automatically supplying the intrinsic redundancy contained in natural communication is explained. The distinctions between adaptive and non-adaptive models of communication are shown and details are given of working predictive text generation systems. One such system is the Reactive Keyboard, and in the second part of the book this is described. It greatly speeds communication by predicting the user's next response, although it does not always predict correctly. The guesses are made on the basis of previous answers and thus can conform to whatever kind of text is entered. This will be of great value to all involved in helping disabled users interact with computers.